Networks are present in many fields such as finance, sociology and transportation. Often these networks are dynamic: they have a temporal aspect such as time of transaction, time of connection or time of packet sending. Therefore, visual exploration of these networks plays an important role in understanding network behavior.
Most visualizations separate the structural aspect from the temporal aspect by using either the concept of animation or grouping small multiples of data calls to show the network behavior over time. However, there are obvious problems with animation such as the difficulty to focus on many items simultaneously and the difficulty to track changes over (longer) time periods. Grouping data by multiples (i.e. dividing time into small chunks) is difficult because it is difficult to determine the number of multiples to use; if too many multiples are used detail is too low and the multiples can become too small, if too few multiples are used the detail of change in subsequent multiples is too high and therefore difficult to compare.
In view of the limitations of animation of large data sets, alternative approaches have been suggested. The concept of a massive sequence view for visualizing the timing and interaction of computer object calls was first introduced D. Jerding and J. Stasko in “The information mural: a technique for displaying and navigating large information spaces” Information Visualization”, 1995. Proceedings., pages 43-50, October 1995 and expanded upon in “The information mural: a technique for displaying and navigating large information spaces”, Visualization and Computer Graphics, IEEE Transactions on, 4(3):257-271, July-September 1998.
The massive sequence view introduced by Jerding et al is an extension of a message sequence chart in which time is mapped to space. In their approach which is used to visualize and analyze program-execution traces each program class c is represented using an (invisible) horizontal line. All lines are positioned equally spaced along the vertical axis. The horizontal axis of the visualization represents chronological order t0 . . . tn. If there is a function call from class ci to class cj at time tk a vertical line is drawn with start and endpoints the y-position of ci and cj respectively, at horizontal position tk. This is repeated for all function calls in the program execution trace. By examining the message trace, users can discover phases in the execution, relationships between classes, and generally how the objects accomplish the functional purpose of the program. In the system discussed by Jerding et al the classes are shown in the order that they are declared in the header files of the program. Later the classes can be listed vertically according to their alphabetical order; by their appearance order in source files; or by user specification.
As there may be more function calls than there are pixels available for display, Jerding et al have proposed that a massive sequence view should be drawn using anti-aliasing techniques and gray scale shading. Jerding et al also proposed extending the massive sequence view by enabling users to interactively control filtering and abstraction and adding brushing techniques and the use of size and color to highlight individual program calls.
The massive sequence view concept has been refined by Holten et al as disclosed in D. Holten, B. Cornelissen, and J. van Wijk, “Trace visualization using hierarchical edge bundles and massive sequence views”, Visualizing Software for Understanding and Analysis, 2007. VISSOFT 2007. 4th IEEE International Workshop, pages 47-54, June 2007 and B. Cornelissen, D. Holten, A. Zaidman, L. Moonen, J. van Wijk, and A. van Deursen, “Understanding execution traces using massive sequence and circular bundle view”, Program Comprehension, 2007. ICPC '07. 15th IEEE International Conference on, pages 49-58, June 2007. Holten et al. extend the massive sequence view in such a way that the visibility of outlier calls is guaranteed when visualizing more than hundreds of thousands of calls using Importance-Based Anti-Aliasing and improved zooming capabilities. In the system discussed by Holten et al classes are ordered based upon a user-defined hierarchy to assist with the visualization of data.
Although these developments have assisted in presenting a visualization of data which enables patterns and outliers to be identified further improvements are desired.